


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


A			:						List[str]        =  logging.get_logger(__name__)  # pylint: disable=invalid-name

A			:						str        =  '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'






def 					UpperCamelCase   (      __magic_name__	:				Dict       ,       __magic_name__	:				Optional[int]       ,       __magic_name__	:				Optional[Any]=8	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__	     =						height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		lowercase__	     =						width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor






def 					UpperCamelCase   (      __magic_name__	:				int       ,       __magic_name__	:				Optional[int]=512       ,       __magic_name__	:				Union[str, Any]=512	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						pil_image.resize((w, h)       ,       resample=Image.BICUBIC       ,       reducing_gap=1	)
		lowercase__	     =						np.array(pil_image.convert("""RGB"""	)	)
		lowercase__	     =						arr.astype(np.floataa	) / 1_2_7.5 - 1
		lowercase__	     =						np.transpose(lowerCAmelCase__       ,       [2, 0, 1]	)
		lowercase__	     =						torch.from_numpy(lowerCAmelCase__	).unsqueeze(0	)
		return image







class 				A       (			_lowerCAmelCase				):
					'''simple docstring'''






					def __init__(self       :		str ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Union[str, Any] ,   )		->  int:




							"""simple docstring"""


							super().__init__()

							self.register_modules(
							    unet=SCREAMING_SNAKE_CASE_ ,   scheduler=SCREAMING_SNAKE_CASE_ ,   movq=SCREAMING_SNAKE_CASE_ ,   )
							lowercase__	     =						2 ** (len(self.movq.config.block_out_channels						) - 1)

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		str						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						min(int(num_inference_steps * strength						) ,   SCREAMING_SNAKE_CASE_						)

							lowercase__	     =						max(num_inference_steps - init_timestep ,   0						)
							lowercase__	     =						self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Any=None						)		->  Optional[Any]:




							"""simple docstring"""


							if not isinstance(SCREAMING_SNAKE_CASE_ ,   (torch.Tensor, PIL.Image.Image, list)						):
									raise ValueError(
									    f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(SCREAMING_SNAKE_CASE_						)}'''						)

							lowercase__	     =						image.to(device=SCREAMING_SNAKE_CASE_ ,   dtype=SCREAMING_SNAKE_CASE_						)

							lowercase__	     =						batch_size * num_images_per_prompt

							if image.shape[1] == 4:
									lowercase__	     =						image

							else:
									if isinstance(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_						) and len(SCREAMING_SNAKE_CASE_						) != batch_size:
											raise ValueError(
											    f'''You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_						)}, but requested an effective batch'''
											    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

									elif isinstance(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_						):
											lowercase__	     =						[
											    self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(SCREAMING_SNAKE_CASE_						)
											]
											lowercase__	     =						torch.cat(SCREAMING_SNAKE_CASE_ ,   dim=0						)
									else:
											lowercase__	     =						self.movq.encode(SCREAMING_SNAKE_CASE_						).latent_dist.sample(SCREAMING_SNAKE_CASE_						)

									lowercase__	     =						self.movq.config.scaling_factor * init_latents

							lowercase__	     =						torch.cat([init_latents] ,   dim=0						)

							lowercase__	     =						init_latents.shape
							lowercase__	     =						randn_tensor(SCREAMING_SNAKE_CASE_ ,   generator=SCREAMING_SNAKE_CASE_ ,   device=SCREAMING_SNAKE_CASE_ ,   dtype=SCREAMING_SNAKE_CASE_						)

							# get latents
							lowercase__	     =						self.scheduler.add_noise(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_						)

							lowercase__	     =						init_latents

							return latents

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		List[Any]=0						)		->  Optional[int]:




							"""simple docstring"""


							if is_accelerate_available():
									from accelerate import cpu_offload
							else:
									raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

							lowercase__	     =						torch.device(f'''cuda:{gpu_id}'''						)

							lowercase__	     =						[
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
											cpu_offload(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_						)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Tuple=0						)		->  Tuple:




							"""simple docstring"""


							if is_accelerate_available() and is_accelerate_version(""">=""" ,   """0.17.0.dev0"""						):
									from accelerate import cpu_offload_with_hook
							else:
									raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""						)

							lowercase__	     =						torch.device(f'''cuda:{gpu_id}'''						)

							if self.device.type != "cpu":
									self.to("""cpu""" ,   silence_dtype_warnings=SCREAMING_SNAKE_CASE_						)
									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							lowercase__	     =						None
							for cpu_offloaded_model in [self.unet, self.movq]:
									lowercase__	     =						cpu_offload_with_hook(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   prev_module_hook=SCREAMING_SNAKE_CASE_						)

							# We'll offload the last model manually.
							lowercase__	     =						hook

					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Any:




							"""simple docstring"""


							if not hasattr(self.unet ,   """_hf_hook"""						):
									return self.device
							for module in self.unet.modules():
									if (
									    hasattr(SCREAMING_SNAKE_CASE_ ,   """_hf_hook"""						)
									    and hasattr(module._hf_hook ,   """execution_device"""						)
									    and module._hf_hook.execution_device is not None
									):
											return torch.device(module._hf_hook.execution_device						)
							return self.device


					@torch.no_grad()
					@replace_example_docstring(SCREAMING_SNAKE_CASE_						)
					def __call__(self       :		List[str] ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		int = 512 ,   _UpperCAmelCase       :		Optional[int] = 512 ,   _UpperCAmelCase       :		str = 100 ,   _UpperCAmelCase       :		Optional[Any] = 4.0 ,   _UpperCAmelCase       :		int = 0.3 ,   _UpperCAmelCase       :		Union[str, Any] = 1 ,   _UpperCAmelCase       :		Optional[Any] = None ,   _UpperCAmelCase       :		Any = "pil" ,   _UpperCAmelCase       :		Optional[Any] = True ,   )		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						self._execution_device

							lowercase__	     =						guidance_scale > 1.0

							if isinstance(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_						):
									lowercase__	     =						torch.cat(SCREAMING_SNAKE_CASE_ ,   dim=0						)
							lowercase__	     =						image_embeds.shape[0]
							if isinstance(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_						):
									lowercase__	     =						torch.cat(SCREAMING_SNAKE_CASE_ ,   dim=0						)

							if do_classifier_free_guidance:
									lowercase__	     =						image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_ ,   dim=0						)
									lowercase__	     =						negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_ ,   dim=0						)

							lowercase__	     =						torch.cat([negative_image_embeds, image_embeds] ,   dim=0						).to(dtype=self.unet.dtype ,   device=SCREAMING_SNAKE_CASE_						)

							if not isinstance(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_						):
									lowercase__	     =						[image]
							if not all(isinstance(SCREAMING_SNAKE_CASE_ ,   (PIL.Image.Image, torch.Tensor)						) for i in image						):
									raise ValueError(
									    f'''Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE_						) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''						)

							lowercase__	     =						torch.cat([prepare_image(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_						) for i in image] ,   dim=0						)
							lowercase__	     =						image.to(dtype=image_embeds.dtype ,   device=SCREAMING_SNAKE_CASE_						)

							lowercase__	     =						self.movq.encode(SCREAMING_SNAKE_CASE_						)["""latents"""]
							lowercase__	     =						latents.repeat_interleave(SCREAMING_SNAKE_CASE_ ,   dim=0						)
							self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ ,   device=SCREAMING_SNAKE_CASE_						)
							lowercase__	     =						self.get_timesteps(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_						)
							lowercase__	     =						timesteps[:1].repeat(batch_size * num_images_per_prompt						)
							lowercase__	     =						downscale_height_and_width(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   self.movq_scale_factor						)
							lowercase__	     =						self.prepare_latents(
							    SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   image_embeds.dtype ,   SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_						)
							for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_						)						):
									# expand the latents if we are doing classifier free guidance
									lowercase__	     =						torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

									lowercase__	     =						{"""image_embeds""": image_embeds}
									lowercase__	     =						self.unet(
									    sample=SCREAMING_SNAKE_CASE_ ,   timestep=SCREAMING_SNAKE_CASE_ ,   encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,   added_cond_kwargs=SCREAMING_SNAKE_CASE_ ,   return_dict=SCREAMING_SNAKE_CASE_ ,   )[0]

									if do_classifier_free_guidance:
											lowercase__	     =						noise_pred.split(latents.shape[1] ,   dim=1						)
											lowercase__	     =						noise_pred.chunk(2						)
											lowercase__	     =						variance_pred.chunk(2						)
											lowercase__	     =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
											lowercase__	     =						torch.cat([noise_pred, variance_pred_text] ,   dim=1						)

									if not (
									    hasattr(self.scheduler.config ,   """variance_type"""						)
									    and self.scheduler.config.variance_type in ["learned", "learned_range"]
									):
											lowercase__	     =						noise_pred.split(latents.shape[1] ,   dim=1						)

									# compute the previous noisy sample x_t -> x_t-1
									lowercase__	     =						self.scheduler.step(
									    SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   generator=SCREAMING_SNAKE_CASE_ ,   )[0]

							# post-processing
							lowercase__	     =						self.movq.decode(SCREAMING_SNAKE_CASE_ ,   force_not_quantize=SCREAMING_SNAKE_CASE_						)["""sample"""]

							if output_type not in ["pt", "np", "pil"]:
									raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''						)

							if output_type in ["np", "pil"]:
									lowercase__	     =						image * 0.5 + 0.5
									lowercase__	     =						image.clamp(0 ,   1						)
									lowercase__	     =						image.cpu().permute(0 ,   2 ,   3 ,   1						).float().numpy()

							if output_type == "pil":
									lowercase__	     =						self.numpy_to_pil(SCREAMING_SNAKE_CASE_						)

							if not return_dict:
									return (image,)

							return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_						)






#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist







def  lowerCAmelCase_	(*lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			with open(lowerCAmelCase__		,						"""r"""  ) as fh:
						fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_EX  )
						try:
									print(*lowerCAmelCase__  )
						finally:
									fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_UN  )


a      :  Tuple			     =			int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
a      :  Union[str, Any]			     =			torch.device('cuda', local_rank)
a      :  Any			     =			socket.gethostname()

a      :  Optional[Any]			     =			F'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	a      :  Optional[Any]			     =			dist.get_rank()
	a      :  List[Any]			     =			dist.get_world_size()

	printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(F'''{gpu} is broken''')
	raise






